As part of their IFRS17 solution, insurers will need to include an allocation engine. Given the data volumes and need to manage many groups a manual solution would not be advisable. Integrating the allocation engine into the overall IFRS17 solution would also be preferable given the interrelated nature of many of the calculations.
Onerous contract testing for initial grouping and subsequent measurement will rely on such allocations, which must be robust and transparent. It will be desirable to avoid having to transfer allocated numbers across applications mid calculation. Another key consideration is that a prime source of the allocation drivers may well be the IFRS17 sub-ledger balances themselves.
The prime role of the allocation engine is to take a percentage and apply it to an aggregated number so it can be allocated at a more granular level. The percentages need to be held in transparent and auditable tables and potentially easily updated at each reporting cycle. The engine should also have the capability to calculate the percentages based on drivers, either loaded directly or potentially derived from balances with the sub-ledger or general ledger. The allocation engine should be able to handle significant data volumes and operate at speed so as not to become a bottle neck in the month end process.
Under IFRS17 insurance contracts are required to be grouped. The grouping rules are defined by the standard. This makes sense from an accounting point of view however the models and data the insurers maintain may not support such grouping.
Some examples might be; risk calculations are applied at a more aggregated level that the IFRS17 groups, expenses are managed in the general ledger by category which are not by group, acquisition costs may be paid in relation to groups of policies that do not map to the IFRS17 groups.
Given these challenges insurers need to consider how to cut these items into the relevant groups. Some insurers may wish to maintain even more granular data views, such as by policy, to enable insight and analytics across more business dimensions.